Overview

Dataset statistics

Number of variables20
Number of observations166158
Missing cells27348
Missing cells (%)0.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.7 MiB
Average record size in memory433.7 B

Variable types

Categorical4
Numeric13
Text2
DateTime1

Alerts

year has constant value ""Constant
dep_time is highly overall correlated with sched_dep_time and 3 other fieldsHigh correlation
sched_dep_time is highly overall correlated with dep_time and 3 other fieldsHigh correlation
dep_delay is highly overall correlated with arr_delayHigh correlation
arr_time is highly overall correlated with dep_time and 3 other fieldsHigh correlation
sched_arr_time is highly overall correlated with dep_time and 3 other fieldsHigh correlation
arr_delay is highly overall correlated with dep_delayHigh correlation
flight is highly overall correlated with carrierHigh correlation
air_time is highly overall correlated with distanceHigh correlation
distance is highly overall correlated with air_timeHigh correlation
hour is highly overall correlated with dep_time and 3 other fieldsHigh correlation
carrier is highly overall correlated with flight and 1 other fieldsHigh correlation
origin is highly overall correlated with carrierHigh correlation
dep_time has 4883 (2.9%) missing valuesMissing
dep_delay has 4883 (2.9%) missing valuesMissing
arr_time has 5101 (3.1%) missing valuesMissing
arr_delay has 5480 (3.3%) missing valuesMissing
air_time has 5480 (3.3%) missing valuesMissing
dep_delay has 7963 (4.8%) zerosZeros
arr_delay has 2599 (1.6%) zerosZeros
minute has 30802 (18.5%) zerosZeros

Reproduction

Analysis started2023-11-08 01:23:40.511128
Analysis finished2023-11-08 01:24:06.551669
Duration26.04 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

year
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.7 MiB
2013
166158 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters664632
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2013 166158
100.0%

Length

2023-11-07T17:24:06.674316image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T17:24:06.778326image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2013 166158
100.0%

Most occurring characters

ValueCountFrequency (%)
2 166158
25.0%
0 166158
25.0%
1 166158
25.0%
3 166158
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 664632
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 166158
25.0%
0 166158
25.0%
1 166158
25.0%
3 166158
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 664632
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 166158
25.0%
0 166158
25.0%
1 166158
25.0%
3 166158
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 664632
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 166158
25.0%
0 166158
25.0%
1 166158
25.0%
3 166158
25.0%

month
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5518362
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:06.871106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6997793
Coefficient of variation (CV)0.47856353
Kurtosis-1.2445938
Mean3.5518362
Median Absolute Deviation (MAD)1
Skewness-0.051966521
Sum590166
Variance2.8892495
MonotonicityIncreasing
2023-11-07T17:24:06.978427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 28834
17.4%
5 28796
17.3%
4 28330
17.1%
6 28243
17.0%
1 27004
16.3%
2 24951
15.0%
ValueCountFrequency (%)
1 27004
16.3%
2 24951
15.0%
3 28834
17.4%
4 28330
17.1%
5 28796
17.3%
6 28243
17.0%
ValueCountFrequency (%)
6 28243
17.0%
5 28796
17.3%
4 28330
17.1%
3 28834
17.4%
2 24951
15.0%
1 27004
16.3%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.642515
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:07.096166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7240706
Coefficient of variation (CV)0.55771534
Kurtosis-1.1847736
Mean15.642515
Median Absolute Deviation (MAD)8
Skewness0.0085704498
Sum2599129
Variance76.109407
MonotonicityNot monotonic
2023-11-07T17:24:07.222852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
28 5757
 
3.5%
21 5727
 
3.4%
14 5727
 
3.4%
7 5686
 
3.4%
24 5658
 
3.4%
17 5640
 
3.4%
10 5623
 
3.4%
3 5593
 
3.4%
22 5593
 
3.4%
15 5590
 
3.4%
Other values (21) 109564
65.9%
ValueCountFrequency (%)
1 5414
3.3%
2 5267
3.2%
3 5593
3.4%
4 5514
3.3%
5 5444
3.3%
6 5431
3.3%
7 5686
3.4%
8 5533
3.3%
9 5215
3.1%
10 5623
3.4%
ValueCountFrequency (%)
31 2811
1.7%
30 4536
2.7%
29 4633
2.8%
28 5757
3.5%
27 5425
3.3%
26 5296
3.2%
25 5565
3.3%
24 5658
3.4%
23 5283
3.2%
22 5593
3.4%

dep_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1291
Distinct (%)0.8%
Missing4883
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1351.7406
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:07.363836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile623
Q1909
median1407
Q31748
95-th percentile2114
Maximum2400
Range2399
Interquartile range (IQR)839

Descriptive statistics

Standard deviation490.41005
Coefficient of variation (CV)0.36279895
Kurtosis-1.0828551
Mean1351.7406
Median Absolute Deviation (MAD)425
Skewness-0.039285285
Sum2.1800196 × 108
Variance240502.02
MonotonicityNot monotonic
2023-11-07T17:24:07.508449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 441
 
0.3%
557 420
 
0.3%
556 416
 
0.3%
655 393
 
0.2%
855 385
 
0.2%
755 376
 
0.2%
1455 363
 
0.2%
654 361
 
0.2%
856 358
 
0.2%
558 357
 
0.2%
Other values (1281) 157405
94.7%
(Missing) 4883
 
2.9%
ValueCountFrequency (%)
1 14
< 0.1%
2 26
< 0.1%
3 15
< 0.1%
4 15
< 0.1%
5 12
< 0.1%
6 8
 
< 0.1%
7 12
< 0.1%
8 12
< 0.1%
9 10
 
< 0.1%
10 12
< 0.1%
ValueCountFrequency (%)
2400 11
 
< 0.1%
2359 29
< 0.1%
2358 38
< 0.1%
2357 36
< 0.1%
2356 43
< 0.1%
2355 54
< 0.1%
2354 40
< 0.1%
2353 38
< 0.1%
2352 32
< 0.1%
2351 28
< 0.1%

sched_dep_time
Real number (ℝ)

HIGH CORRELATION 

Distinct969
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346.8721
Minimum500
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:07.663134image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile630
Q1909
median1400
Q31730
95-th percentile2045
Maximum2359
Range1859
Interquartile range (IQR)821

Descriptive statistics

Standard deviation468.25773
Coefficient of variation (CV)0.3476631
Kurtosis-1.2029954
Mean1346.8721
Median Absolute Deviation (MAD)410
Skewness-0.020562276
Sum2.2379358 × 108
Variance219265.31
MonotonicityNot monotonic
2023-11-07T17:24:07.840659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 3702
 
2.2%
900 2558
 
1.5%
700 2463
 
1.5%
630 2407
 
1.4%
1700 2407
 
1.4%
1200 2388
 
1.4%
1600 2216
 
1.3%
2000 1848
 
1.1%
1900 1768
 
1.1%
1300 1739
 
1.0%
Other values (959) 142662
85.9%
ValueCountFrequency (%)
500 176
0.1%
501 1
 
< 0.1%
505 1
 
< 0.1%
510 4
 
< 0.1%
515 113
0.1%
520 1
 
< 0.1%
525 35
 
< 0.1%
527 1
 
< 0.1%
529 3
 
< 0.1%
530 102
0.1%
ValueCountFrequency (%)
2359 380
0.2%
2358 44
 
< 0.1%
2355 62
 
< 0.1%
2352 16
 
< 0.1%
2345 1
 
< 0.1%
2339 1
 
< 0.1%
2315 1
 
< 0.1%
2300 11
 
< 0.1%
2258 17
 
< 0.1%
2255 180
0.1%

dep_delay
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct469
Distinct (%)0.3%
Missing4883
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean13.715666
Minimum-33
Maximum1301
Zeros7963
Zeros (%)4.8%
Negative88828
Negative (%)53.5%
Memory size1.3 MiB
2023-11-07T17:24:07.990730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-33
5-th percentile-9
Q1-5
median-1
Q312
95-th percentile93
Maximum1301
Range1334
Interquartile range (IQR)17

Descriptive statistics

Standard deviation41.677452
Coefficient of variation (CV)3.0386751
Kurtosis46.419289
Mean13.715666
Median Absolute Deviation (MAD)5
Skewness4.7822367
Sum2211994
Variance1737.01
MonotonicityNot monotonic
2023-11-07T17:24:08.261788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 12104
 
7.3%
-4 12052
 
7.3%
-3 11785
 
7.1%
-2 10540
 
6.3%
-6 9939
 
6.0%
-1 9150
 
5.5%
-7 8077
 
4.9%
0 7963
 
4.8%
-8 5639
 
3.4%
1 3964
 
2.4%
Other values (459) 70062
42.2%
(Missing) 4883
 
2.9%
ValueCountFrequency (%)
-33 1
 
< 0.1%
-30 1
 
< 0.1%
-27 1
 
< 0.1%
-25 1
 
< 0.1%
-24 3
 
< 0.1%
-23 1
 
< 0.1%
-22 5
 
< 0.1%
-21 6
 
< 0.1%
-20 21
< 0.1%
-19 10
< 0.1%
ValueCountFrequency (%)
1301 1
 
< 0.1%
1137 1
 
< 0.1%
1126 1
 
< 0.1%
960 1
 
< 0.1%
911 1
 
< 0.1%
899 1
 
< 0.1%
878 1
 
< 0.1%
853 3
< 0.1%
812 1
 
< 0.1%
803 1
 
< 0.1%

arr_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1392
Distinct (%)0.9%
Missing5101
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean1503.949
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:08.407025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile732
Q11106
median1538
Q31945
95-th percentile2249
Maximum2400
Range2399
Interquartile range (IQR)839

Descriptive statistics

Standard deviation536.36355
Coefficient of variation (CV)0.35663679
Kurtosis-0.17961664
Mean1503.949
Median Absolute Deviation (MAD)420
Skewness-0.48486168
Sum2.4222152 × 108
Variance287685.86
MonotonicityNot monotonic
2023-11-07T17:24:08.552635image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1006 240
 
0.1%
1008 235
 
0.1%
1012 231
 
0.1%
1005 230
 
0.1%
1013 230
 
0.1%
1015 228
 
0.1%
2017 225
 
0.1%
2029 224
 
0.1%
1049 222
 
0.1%
1016 222
 
0.1%
Other values (1382) 158770
95.6%
(Missing) 5101
 
3.1%
ValueCountFrequency (%)
1 94
0.1%
2 79
< 0.1%
3 72
< 0.1%
4 93
0.1%
5 104
0.1%
6 64
< 0.1%
7 93
0.1%
8 69
< 0.1%
9 75
< 0.1%
10 92
0.1%
ValueCountFrequency (%)
2400 79
< 0.1%
2359 120
0.1%
2358 88
0.1%
2357 102
0.1%
2356 93
0.1%
2355 95
0.1%
2354 110
0.1%
2353 83
< 0.1%
2352 94
0.1%
2351 85
0.1%

sched_arr_time
Real number (ℝ)

HIGH CORRELATION 

Distinct1134
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1539.4886
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:08.692292image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile815
Q11125
median1600
Q31950
95-th percentile2245
Maximum2359
Range2358
Interquartile range (IQR)825

Descriptive statistics

Standard deviation498.56213
Coefficient of variation (CV)0.32384919
Kurtosis-0.36727828
Mean1539.4886
Median Absolute Deviation (MAD)416
Skewness-0.36956113
Sum2.5579835 × 108
Variance248564.2
MonotonicityNot monotonic
2023-11-07T17:24:08.830426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1820 705
 
0.4%
2015 705
 
0.4%
2050 703
 
0.4%
1015 657
 
0.4%
1110 629
 
0.4%
1640 597
 
0.4%
1220 584
 
0.4%
2240 583
 
0.4%
1305 578
 
0.3%
1645 578
 
0.3%
Other values (1124) 159839
96.2%
ValueCountFrequency (%)
1 152
0.1%
2 83
< 0.1%
3 68
< 0.1%
4 74
< 0.1%
5 78
< 0.1%
6 17
 
< 0.1%
7 31
 
< 0.1%
8 55
 
< 0.1%
9 13
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
2359 364
0.2%
2358 197
0.1%
2357 305
0.2%
2356 264
0.2%
2355 171
0.1%
2354 128
 
0.1%
2353 121
 
0.1%
2352 14
 
< 0.1%
2351 50
 
< 0.1%
2350 42
 
< 0.1%

arr_delay
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct526
Distinct (%)0.3%
Missing5480
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean8.1512902
Minimum-86
Maximum1272
Zeros2599
Zeros (%)1.6%
Negative90709
Negative (%)54.6%
Memory size1.3 MiB
2023-11-07T17:24:08.969259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-32
Q1-16
median-4
Q315
95-th percentile96
Maximum1272
Range1358
Interquartile range (IQR)31

Descriptive statistics

Standard deviation46.00005
Coefficient of variation (CV)5.6432845
Kurtosis31.148268
Mean8.1512902
Median Absolute Deviation (MAD)15
Skewness3.723021
Sum1309733
Variance2116.0046
MonotonicityNot monotonic
2023-11-07T17:24:09.120633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12 3441
 
2.1%
-13 3419
 
2.1%
-10 3348
 
2.0%
-11 3341
 
2.0%
-14 3332
 
2.0%
-15 3307
 
2.0%
-8 3247
 
2.0%
-7 3240
 
1.9%
-9 3231
 
1.9%
-17 3187
 
1.9%
Other values (516) 127585
76.8%
(Missing) 5480
 
3.3%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-79 1
 
< 0.1%
-75 2
 
< 0.1%
-74 1
 
< 0.1%
-73 1
 
< 0.1%
-71 3
 
< 0.1%
-70 8
< 0.1%
-69 7
< 0.1%
-68 9
< 0.1%
-67 2
 
< 0.1%
ValueCountFrequency (%)
1272 1
< 0.1%
1127 1
< 0.1%
1109 1
< 0.1%
931 1
< 0.1%
915 1
< 0.1%
875 1
< 0.1%
852 1
< 0.1%
851 1
< 0.1%
850 1
< 0.1%
834 1
< 0.1%

carrier
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
UA
28936 
B6
27017 
EV
26558 
DL
23623 
AA
16380 
Other values (11)
43644 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters332316
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUA
2nd rowUA
3rd rowAA
4th rowB6
5th rowDL

Common Values

ValueCountFrequency (%)
UA 28936
17.4%
B6 27017
16.3%
EV 26558
16.0%
DL 23623
14.2%
AA 16380
9.9%
MQ 13244
8.0%
US 10123
 
6.1%
9E 9069
 
5.5%
WN 5919
 
3.6%
VX 2332
 
1.4%
Other values (6) 2957
 
1.8%

Length

2023-11-07T17:24:09.255844image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ua 28936
17.4%
b6 27017
16.3%
ev 26558
16.0%
dl 23623
14.2%
aa 16380
9.9%
mq 13244
8.0%
us 10123
 
6.1%
9e 9069
 
5.5%
wn 5919
 
3.6%
vx 2332
 
1.4%
Other values (6) 2957
 
1.8%

Most occurring characters

ValueCountFrequency (%)
A 62239
18.7%
U 39059
11.8%
E 35627
10.7%
V 29138
8.8%
B 27017
8.1%
6 27017
8.1%
L 25451
7.7%
D 23623
 
7.1%
Q 13244
 
4.0%
M 13244
 
4.0%
Other values (9) 36657
11.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 295895
89.0%
Decimal Number 36421
 
11.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 62239
21.0%
U 39059
13.2%
E 35627
12.0%
V 29138
9.8%
B 27017
9.1%
L 25451
8.6%
D 23623
 
8.0%
Q 13244
 
4.5%
M 13244
 
4.5%
S 10485
 
3.5%
Other values (7) 16768
 
5.7%
Decimal Number
ValueCountFrequency (%)
6 27017
74.2%
9 9404
 
25.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 295895
89.0%
Common 36421
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 62239
21.0%
U 39059
13.2%
E 35627
12.0%
V 29138
9.8%
B 27017
9.1%
L 25451
8.6%
D 23623
 
8.0%
Q 13244
 
4.5%
M 13244
 
4.5%
S 10485
 
3.5%
Other values (7) 16768
 
5.7%
Common
ValueCountFrequency (%)
6 27017
74.2%
9 9404
 
25.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 332316
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 62239
18.7%
U 39059
11.8%
E 35627
10.7%
V 29138
8.8%
B 27017
8.1%
6 27017
8.1%
L 25451
7.7%
D 23623
 
7.1%
Q 13244
 
4.0%
M 13244
 
4.0%
Other values (9) 36657
11.0%

flight
Real number (ℝ)

HIGH CORRELATION 

Distinct2994
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.2587
Minimum1
Maximum8500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:09.383475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile75
Q1553
median1464
Q33697
95-th percentile4662
Maximum8500
Range8499
Interquartile range (IQR)3144

Descriptive statistics

Standard deviation1655.0415
Coefficient of variation (CV)0.84001232
Kurtosis-1.0366403
Mean1970.2587
Median Absolute Deviation (MAD)1062
Skewness0.62160158
Sum3.2737424 × 108
Variance2739162.5
MonotonicityNot monotonic
2023-11-07T17:24:09.525537image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181 515
 
0.3%
695 475
 
0.3%
27 413
 
0.2%
15 411
 
0.2%
21 390
 
0.2%
345 389
 
0.2%
301 382
 
0.2%
1109 379
 
0.2%
731 371
 
0.2%
269 371
 
0.2%
Other values (2984) 162062
97.5%
ValueCountFrequency (%)
1 274
0.2%
2 51
 
< 0.1%
3 224
0.1%
4 275
0.2%
5 11
 
< 0.1%
6 210
0.1%
7 182
0.1%
8 171
0.1%
9 153
0.1%
10 61
 
< 0.1%
ValueCountFrequency (%)
8500 1
 
< 0.1%
6180 6
 
< 0.1%
6177 13
< 0.1%
6171 1
 
< 0.1%
6165 1
 
< 0.1%
6138 2
 
< 0.1%
6055 29
< 0.1%
6054 25
< 0.1%
6012 6
 
< 0.1%
6002 1
 
< 0.1%
Distinct3825
Distinct (%)2.3%
Missing1521
Missing (%)0.9%
Memory size9.9 MiB
2023-11-07T17:24:09.771354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9950679
Min length5

Characters and Unicode

Total characters987010
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)0.1%

Sample

1st rowN14228
2nd rowN24211
3rd rowN619AA
4th rowN804JB
5th rowN668DN
ValueCountFrequency (%)
n725mq 393
 
0.2%
n723mq 390
 
0.2%
n713mq 382
 
0.2%
n722mq 372
 
0.2%
n711mq 369
 
0.2%
n738mq 228
 
0.1%
n351jb 227
 
0.1%
n228jb 220
 
0.1%
n258jb 218
 
0.1%
n542mq 214
 
0.1%
Other values (3815) 161624
98.2%
2023-11-07T17:24:10.148480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 177572
18.0%
3 73333
 
7.4%
1 72712
 
7.4%
5 67430
 
6.8%
A 58355
 
5.9%
7 57315
 
5.8%
2 55710
 
5.6%
9 51897
 
5.3%
6 51091
 
5.2%
4 50146
 
5.1%
Other values (24) 271449
27.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 554108
56.1%
Uppercase Letter 432902
43.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 177572
41.0%
A 58355
 
13.5%
B 33354
 
7.7%
J 32651
 
7.5%
U 22310
 
5.2%
W 18055
 
4.2%
Q 14650
 
3.4%
M 13946
 
3.2%
D 11828
 
2.7%
S 6856
 
1.6%
Other values (14) 43325
 
10.0%
Decimal Number
ValueCountFrequency (%)
3 73333
13.2%
1 72712
13.1%
5 67430
12.2%
7 57315
10.3%
2 55710
10.1%
9 51897
9.4%
6 51091
9.2%
4 50146
9.0%
8 40518
7.3%
0 33956
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 554108
56.1%
Latin 432902
43.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 177572
41.0%
A 58355
 
13.5%
B 33354
 
7.7%
J 32651
 
7.5%
U 22310
 
5.2%
W 18055
 
4.2%
Q 14650
 
3.4%
M 13946
 
3.2%
D 11828
 
2.7%
S 6856
 
1.6%
Other values (14) 43325
 
10.0%
Common
ValueCountFrequency (%)
3 73333
13.2%
1 72712
13.1%
5 67430
12.2%
7 57315
10.3%
2 55710
10.1%
9 51897
9.4%
6 51091
9.2%
4 50146
9.0%
8 40518
7.3%
0 33956
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 987010
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 177572
18.0%
3 73333
 
7.4%
1 72712
 
7.4%
5 67430
 
6.8%
A 58355
 
5.9%
7 57315
 
5.8%
2 55710
 
5.6%
9 51897
 
5.3%
6 51091
 
5.2%
4 50146
 
5.1%
Other values (24) 271449
27.5%

origin
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
EWR
60718 
JFK
55366 
LGA
50074 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters498474
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEWR
2nd rowLGA
3rd rowJFK
4th rowJFK
5th rowLGA

Common Values

ValueCountFrequency (%)
EWR 60718
36.5%
JFK 55366
33.3%
LGA 50074
30.1%

Length

2023-11-07T17:24:10.288476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T17:24:10.395163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
ewr 60718
36.5%
jfk 55366
33.3%
lga 50074
30.1%

Most occurring characters

ValueCountFrequency (%)
E 60718
12.2%
W 60718
12.2%
R 60718
12.2%
J 55366
11.1%
F 55366
11.1%
K 55366
11.1%
L 50074
10.0%
G 50074
10.0%
A 50074
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 498474
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 60718
12.2%
W 60718
12.2%
R 60718
12.2%
J 55366
11.1%
F 55366
11.1%
K 55366
11.1%
L 50074
10.0%
G 50074
10.0%
A 50074
10.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 498474
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 60718
12.2%
W 60718
12.2%
R 60718
12.2%
J 55366
11.1%
F 55366
11.1%
K 55366
11.1%
L 50074
10.0%
G 50074
10.0%
A 50074
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 60718
12.2%
W 60718
12.2%
R 60718
12.2%
J 55366
11.1%
F 55366
11.1%
K 55366
11.1%
L 50074
10.0%
G 50074
10.0%
A 50074
10.0%

dest
Text

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.5 MiB
2023-11-07T17:24:10.564815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters498474
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIAH
2nd rowIAH
3rd rowMIA
4th rowBQN
5th rowATL
ValueCountFrequency (%)
atl 8538
 
5.1%
ord 8354
 
5.0%
bos 7695
 
4.6%
lax 7632
 
4.6%
mco 7058
 
4.2%
clt 6671
 
4.0%
fll 6404
 
3.9%
sfo 6078
 
3.7%
mia 5816
 
3.5%
dca 5117
 
3.1%
Other values (90) 96795
58.3%
2023-11-07T17:24:10.869636image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 52443
 
10.5%
L 46722
 
9.4%
S 41400
 
8.3%
D 38823
 
7.8%
O 33383
 
6.7%
C 31688
 
6.4%
T 30575
 
6.1%
M 29102
 
5.8%
I 21151
 
4.2%
F 20852
 
4.2%
Other values (16) 152335
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 498474
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 52443
 
10.5%
L 46722
 
9.4%
S 41400
 
8.3%
D 38823
 
7.8%
O 33383
 
6.7%
C 31688
 
6.4%
T 30575
 
6.1%
M 29102
 
5.8%
I 21151
 
4.2%
F 20852
 
4.2%
Other values (16) 152335
30.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 498474
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 52443
 
10.5%
L 46722
 
9.4%
S 41400
 
8.3%
D 38823
 
7.8%
O 33383
 
6.7%
C 31688
 
6.4%
T 30575
 
6.1%
M 29102
 
5.8%
I 21151
 
4.2%
F 20852
 
4.2%
Other values (16) 152335
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 498474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 52443
 
10.5%
L 46722
 
9.4%
S 41400
 
8.3%
D 38823
 
7.8%
O 33383
 
6.7%
C 31688
 
6.4%
T 30575
 
6.1%
M 29102
 
5.8%
I 21151
 
4.2%
F 20852
 
4.2%
Other values (16) 152335
30.6%

air_time
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct484
Distinct (%)0.3%
Missing5480
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean150.56438
Minimum20
Maximum695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:11.016271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q182
median131
Q3191
95-th percentile340
Maximum695
Range675
Interquartile range (IQR)109

Descriptive statistics

Standard deviation93.399122
Coefficient of variation (CV)0.62032683
Kurtosis0.99508681
Mean150.56438
Median Absolute Deviation (MAD)53
Skewness1.0778154
Sum24192383
Variance8723.396
MonotonicityNot monotonic
2023-11-07T17:24:11.166277image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 1272
 
0.8%
43 1259
 
0.8%
45 1240
 
0.7%
47 1233
 
0.7%
41 1218
 
0.7%
44 1206
 
0.7%
40 1183
 
0.7%
39 1178
 
0.7%
46 1177
 
0.7%
109 1172
 
0.7%
Other values (474) 148540
89.4%
(Missing) 5480
 
3.3%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 10
 
< 0.1%
22 27
 
< 0.1%
23 50
 
< 0.1%
24 65
< 0.1%
25 74
< 0.1%
26 96
0.1%
27 84
0.1%
28 120
0.1%
29 141
0.1%
ValueCountFrequency (%)
695 1
< 0.1%
691 1
< 0.1%
686 2
< 0.1%
683 1
< 0.1%
679 1
< 0.1%
676 1
< 0.1%
671 2
< 0.1%
667 1
< 0.1%
666 1
< 0.1%
665 1
< 0.1%

distance
Real number (ℝ)

HIGH CORRELATION 

Distinct203
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1026.7442
Minimum80
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:11.314972image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile199
Q1502
median872
Q31389
95-th percentile2475
Maximum4983
Range4903
Interquartile range (IQR)887

Descriptive statistics

Standard deviation726.05415
Coefficient of variation (CV)0.70714221
Kurtosis1.4018418
Mean1026.7442
Median Absolute Deviation (MAD)389
Skewness1.1600017
Sum1.7060176 × 108
Variance527154.63
MonotonicityNot monotonic
2023-11-07T17:24:11.458596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2475 5554
 
3.3%
762 5208
 
3.1%
733 4149
 
2.5%
2586 3977
 
2.4%
719 3061
 
1.8%
187 2902
 
1.7%
184 2829
 
1.7%
1096 2789
 
1.7%
544 2763
 
1.7%
944 2748
 
1.7%
Other values (193) 130178
78.3%
ValueCountFrequency (%)
80 49
 
< 0.1%
94 623
 
0.4%
96 205
 
0.1%
116 311
 
0.2%
143 285
 
0.2%
160 257
 
0.2%
169 426
 
0.3%
173 57
 
< 0.1%
184 2829
1.7%
185 15
 
< 0.1%
ValueCountFrequency (%)
4983 181
 
0.1%
4963 181
 
0.1%
2586 3977
2.4%
2576 142
 
0.1%
2569 147
 
0.1%
2565 2101
 
1.3%
2521 137
 
0.1%
2475 5554
3.3%
2465 518
 
0.3%
2454 2422
1.5%

hour
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.208964
Minimum5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:11.579777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6
Q19
median14
Q317
95-th percentile20
Maximum23
Range18
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.6691392
Coefficient of variation (CV)0.35348262
Kurtosis-1.2133094
Mean13.208964
Median Absolute Deviation (MAD)4
Skewness-0.015305656
Sum2194775
Variance21.80086
MonotonicityNot monotonic
2023-11-07T17:24:11.699485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
6 13109
 
7.9%
8 13071
 
7.9%
17 12210
 
7.3%
15 11936
 
7.2%
16 11543
 
6.9%
7 11029
 
6.6%
19 10958
 
6.6%
18 10857
 
6.5%
14 10420
 
6.3%
9 10318
 
6.2%
Other values (9) 50707
30.5%
ValueCountFrequency (%)
5 938
 
0.6%
6 13109
7.9%
7 11029
6.6%
8 13071
7.9%
9 10318
6.2%
10 7919
4.8%
11 7926
4.8%
12 8783
5.3%
13 9726
5.9%
14 10420
6.3%
ValueCountFrequency (%)
23 516
 
0.3%
22 1299
 
0.8%
21 5336
3.2%
20 8264
5.0%
19 10958
6.6%
18 10857
6.5%
17 12210
7.3%
16 11543
6.9%
15 11936
7.2%
14 10420
6.3%

minute
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.975752
Minimum0
Maximum59
Zeros30802
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-11-07T17:24:11.839078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median29
Q344
95-th percentile57
Maximum59
Range59
Interquartile range (IQR)37

Descriptive statistics

Standard deviation19.279158
Coefficient of variation (CV)0.74219827
Kurtosis-1.2421519
Mean25.975752
Median Absolute Deviation (MAD)16
Skewness0.099749287
Sum4316079
Variance371.68594
MonotonicityNot monotonic
2023-11-07T17:24:11.983715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30802
18.5%
30 15979
 
9.6%
45 10734
 
6.5%
15 9559
 
5.8%
55 9295
 
5.6%
10 7651
 
4.6%
59 7271
 
4.4%
40 7262
 
4.4%
35 7179
 
4.3%
5 6875
 
4.1%
Other values (50) 53551
32.2%
ValueCountFrequency (%)
0 30802
18.5%
1 927
 
0.6%
2 419
 
0.3%
3 771
 
0.5%
4 626
 
0.4%
5 6875
 
4.1%
6 683
 
0.4%
7 483
 
0.3%
8 896
 
0.5%
9 792
 
0.5%
ValueCountFrequency (%)
59 7271
4.4%
58 700
 
0.4%
57 793
 
0.5%
56 770
 
0.5%
55 9295
5.6%
54 554
 
0.3%
53 599
 
0.4%
52 541
 
0.3%
51 539
 
0.3%
50 6351
3.8%
Distinct3439
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Minimum2013-01-01 05:00:00
Maximum2013-06-30 23:00:00
2023-11-07T17:24:12.124397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:12.390690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

lateflight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
0
98788 
1
67370 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters166158
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98788
59.5%
1 67370
40.5%

Length

2023-11-07T17:24:12.528423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T17:24:12.631176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 98788
59.5%
1 67370
40.5%

Most occurring characters

ValueCountFrequency (%)
0 98788
59.5%
1 67370
40.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166158
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98788
59.5%
1 67370
40.5%

Most occurring scripts

ValueCountFrequency (%)
Common 166158
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98788
59.5%
1 67370
40.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98788
59.5%
1 67370
40.5%

Interactions

2023-11-07T17:24:04.014439image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:45.867659image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.361761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.839446image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.295599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.940769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.438671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.923725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.386490image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.984400image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.417721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.921495image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.389093image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.126140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:45.984040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.470498image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.951340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.409323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.055462image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.550689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.033350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.499187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.091063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.529460image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.029183image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.503038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.244823image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.096738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.583196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.063018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.531966image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.169296image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.665381image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.146036image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.614789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.202765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.647639image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.142906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.620723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.355499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.208836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.691967image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.173749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.644694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.283410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.773040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.255728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.726521image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.310049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.763497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.261592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.734419image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.474224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.327813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.815438image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.290437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.767260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.403850image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.893507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.373441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.846171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.426644image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.884302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.381271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.857440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.591480image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.440512image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.934125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.404105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.886947image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.518076image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.005208image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.487109image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.958896image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.537348image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.000376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.492943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.092333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.701156image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.547936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.043936image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.510820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.998619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.629778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.121897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.594777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.186533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.646057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.113046image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.602677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.203578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.811860image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.654652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.152646image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.615390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.111345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.740508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.230578image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.701497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.293276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.753852image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.222780image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.710121image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.317247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:04.924459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.777323image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.267312image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.727064image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.349707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.855202image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.351256image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.814136image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.404977image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.865554image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.339265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.822817image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.435354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:05.035551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:46.890022image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.374380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.834435image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.462037image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:52.964909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.459965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:55.920260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.513686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:58.966729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.449459image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:01.928301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.546614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:05.152751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.016684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.493399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:49.954142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.583230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.083654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.580642image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.035945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.634336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.080110image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.567802image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.046980image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.667321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:05.263614image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.131377image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.604105image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.063247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.697922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.198342image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.689378image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.143263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.748032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.188791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.682139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.156687image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.779041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:05.381298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:47.248065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:48.724753image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:50.179935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:51.821116image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:53.320021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:54.811054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:56.272917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:57.867740image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:23:59.306019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:00.803813image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:02.278361image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T17:24:03.898748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-07T17:24:12.719910image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
monthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delayflightair_timedistancehourminutecarrieroriginlateflight
month1.0000.0060.0020.0030.062-0.026-0.0130.008-0.001-0.0330.0190.0010.0350.0150.0080.066
day0.0061.0000.0010.0020.037-0.010-0.0010.0360.004-0.004-0.0010.0010.0030.0000.0000.104
dep_time0.0020.0011.0000.9690.2930.7960.8720.2120.032-0.038-0.0360.9670.0960.1090.1120.222
sched_dep_time0.0030.0020.9691.0000.2340.7770.8780.1620.028-0.036-0.0410.9980.1000.1380.1430.144
dep_delay0.0620.0370.2930.2341.0000.1900.2180.636-0.0150.0530.0520.2310.0680.0390.0220.249
arr_time-0.026-0.0100.7960.7770.1901.0000.8660.1230.0170.0550.0500.7750.0580.1070.1150.246
sched_arr_time-0.013-0.0010.8720.8780.2180.8661.0000.1270.0060.0750.0690.8760.0640.1370.1400.140
arr_delay0.0080.0360.2120.1620.6360.1230.1271.0000.087-0.052-0.0990.1610.0270.0480.0330.419
flight-0.0010.0040.0320.028-0.0150.0170.0060.0871.000-0.468-0.4730.028-0.0130.5040.3200.079
air_time-0.033-0.004-0.038-0.0360.0530.0550.075-0.052-0.4681.0000.985-0.0400.0420.3870.2440.094
distance0.019-0.001-0.036-0.0410.0520.0500.069-0.099-0.4730.9851.000-0.0440.0440.4370.2540.053
hour0.0010.0010.9670.9980.2310.7750.8760.1610.028-0.040-0.0441.0000.0390.1330.1330.144
minute0.0350.0030.0960.1000.0680.0580.0640.027-0.0130.0420.0440.0391.0000.1090.1250.040
carrier0.0150.0000.1090.1380.0390.1070.1370.0480.5040.3870.4370.1330.1091.0000.6130.120
origin0.0080.0000.1120.1430.0220.1150.1400.0330.3200.2440.2540.1330.1250.6131.0000.050
lateflight0.0660.1040.2220.1440.2490.2460.1400.4190.0790.0940.0530.1440.0400.1200.0501.000

Missing values

2023-11-07T17:24:05.558466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T17:24:05.937443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-07T17:24:06.368163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hourlateflight
0201311517.05152.0830.081911.0UA1545N14228EWRIAH227.014005152013-01-01 05:00:001
1201311533.05294.0850.083020.0UA1714N24211LGAIAH227.014165292013-01-01 05:00:001
2201311542.05402.0923.085033.0AA1141N619AAJFKMIA160.010895402013-01-01 05:00:001
3201311544.0545-1.01004.01022-18.0B6725N804JBJFKBQN183.015765452013-01-01 05:00:000
4201311554.0600-6.0812.0837-25.0DL461N668DNLGAATL116.0762602013-01-01 06:00:000
5201311554.0558-4.0740.072812.0UA1696N39463EWRORD150.07195582013-01-01 05:00:001
6201311555.0600-5.0913.085419.0B6507N516JBEWRFLL158.01065602013-01-01 06:00:001
7201311557.0600-3.0709.0723-14.0EV5708N829ASLGAIAD53.0229602013-01-01 06:00:000
8201311557.0600-3.0838.0846-8.0B679N593JBJFKMCO140.0944602013-01-01 06:00:000
9201311558.0600-2.0753.07458.0AA301N3ALAALGAORD138.0733602013-01-01 06:00:001
yearmonthdaydep_timesched_dep_timedep_delayarr_timesched_arr_timearr_delaycarrierflighttailnumorigindestair_timedistancehourminutetime_hourlateflight
1661482013630NaN1500NaNNaN1724NaNEV5599N716EVLGACHSNaN6411502013-06-30 15:00:000
1661492013630NaN1457NaNNaN1653NaNEV5814N12921EWRCLTNaN52914572013-06-30 14:00:000
1661502013630NaN1635NaNNaN1827NaNEV5817N21129EWRMEMNaN94616352013-06-30 16:00:000
1661512013630NaN1405NaNNaN1524NaNEV5712N877ASJFKIADNaN2281452013-06-30 14:00:000
1661522013630NaN1442NaNNaN1608NaNEV5713N836ASLGAIADNaN22914422013-06-30 14:00:000
1661532013630NaN1945NaNNaN2104NaNEV5714N836ASJFKIADNaN22819452013-06-30 19:00:000
1661542013630NaN1610NaNNaN1805NaNEV4092N16147EWRDAYNaN53316102013-06-30 16:00:000
1661552013630NaN1709NaNNaN1856NaNEV4662N16911EWRRDUNaN4161792013-06-30 17:00:000
1661562013630NaN2059NaNNaN2307NaNEV5254N760EVLGADSMNaN103120592013-06-30 20:00:000
1661572013630NaN1915NaNNaN2131NaNEV5268N744EVLGACLTNaN54419152013-06-30 19:00:000